Motion Deblurring in Image Color Enhancement by WGAN

Abstract

Motion deblurring and image enhancement are active research areas over the years. Although the CNN-based model has an advanced state of the art in motion deblurring and image enhancement, it fails to produce multitask results when challenged with the images of challenging illumination conditions. The key idea of this paper is to introduce a novel multitask learning algorithm for image motion deblurring and color enhancement, which enables us to enhance the color effect of an image while eliminating motion blur. To achieve this, we explore the synchronization of processing two tasks for the first time by using the framework of generative adversarial networks (GANs). We add L1 loss to the generator loss to simulate the model to match the target image at the pixel level. To make the generated image closer to the target image at the visual level, we also integrate perceptual style loss into generator loss. After a lot of experiments, we get an effective configuration scheme. The best model trained for about one week has achieved state-of-the-art performance in both deblurring and enhancement. Also, its image processing speed is approximately 1.75 times faster than the best competitor.